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--- |
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pipeline_tag: depth-estimation |
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library_name: torch |
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license: mit |
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--- |
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# TAPIP3D: Tracking Any Point in Persistent 3D Geometry |
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This repository contains the TAPIP3D model as presented in [TAPIP3D: Tracking Any Point in Persistent 3D Geometry](https://huggingface.co/papers/2504.14717). |
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[Project Page](https://tapip3d.github.io) |
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Code: https://github.com/zbww/tapip3d |
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**Overview** |
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TAPIP3D is a method for long-term feed-forward 3D point tracking in monocular RGB and RGB-D video sequences. It introduces a 3D feature cloud representation that lifts image features into a persistent world coordinate space, canceling out camera motion and enabling accurate trajectory estimation across frames. |
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## Demo Usage |
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We provide a simple demo script `inference.py`, along with sample input data located in the `demo_inputs/` directory. The script accepts as input either an `.mp4` video file or an `.npz` file. If providing an `.npz` file, it should follow the following format: |
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- `video`: array of shape (T, H, W, 3), dtype: uint8 |
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- `depths` (optional): array of shape (T, H, W), dtype: float32 |
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- `intrinsics` (optional): array of shape (T, 3, 3), dtype: float32 |
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- `extrinsics` (optional): array of shape (T, 4, 4), dtype: float32 |
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For demonstration purposes, the script uses a 32x32 grid of points at the first frame as queries. |
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### Inference with Monocular Video |
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By providing a video as `--input_path`, the script first runs [MegaSAM](https://github.com/mega-sam/mega-sam) with [MoGe](https://wangrc.site/MoGePage/) to estimate depth maps and camera parameters. Subsequently, the model will process these inputs within the global frame. |
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To run inference: |
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```bash |
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python inference.py --input_path demo_inputs/sheep.mp4 --checkpoint checkpoints/tapip3d_final.pth --resolution_factor 2 |
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``` |
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An npz file will be saved to `outputs/inference/`. To visualize the results: |
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```bash |
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python visualize.py <result_npz_path> |
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``` |
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### Inference with Known Depths and Camera Parameters |
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If an `.npz` file containing all four keys (`rgb`, `depths`, `intrinsics`, `extrinsics`) is provided, the model will operate in an aligned global frame, generating point trajectories in world coordinates. |
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## Citation |
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If you find this project useful, please consider citing: |
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``` |
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@article{tapip3d, |
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title={TAPIP3D: Tracking Any Point in Persistent 3D Geometry}, |
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author={Zhang, Bowei and Ke, Lei and Harley, Adam W and Fragkiadaki, Katerina}, |
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journal={arXiv preprint arXiv:2504.14717}, |
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year={2025} |
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} |
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``` |
